Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
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Conf Proc IEEE Eng Med Biol Soc · Jan 2013
Development of a smart backboard system for real-time feedback during CPR chest compression on a soft back support surface.
The quality of cardiopulmonary resuscitation (CPR) is often inconsistent and frequently fails to meet recommended guidelines. One promising approach to address this problem is for clinicians to use an active feedback device during CPR. ⋯ Based on adult CPR manikin tests it was found that the accuracy of the estimated CC depth for a dual accelerometer feedback system is significantly better (7.3% vs. 24.4%) than for a single accelerometer system on soft back support surfaces, in the absence or presence of a backboard. In conclusion, the algorithm used was found to be suitable for a real-time, dual accelerometer CPR feedback application since it yielded reasonable accuracy in terms of CC depth estimation, even when used on a soft back support surface.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2013
Effects of low amplitude pulsed radiofrequency stimulation with different waveform in rats for neuropathic pain.
Pulsed-radiofrequency (PRF) electrical stimulation has been widely used for chronic pain treatment. It has been demonstrated with advantages of low temperature over traditional continuous radiofrequency (CRF) lesions with higher amplitude and mono polar electrode to treat pain in clinics (frequency 500 KHz, Pulse duration 20 msec, Amplitude 45 V, Treatment 2 min). ⋯ Experimental results of Von Frey Score show that the sinusoidal group has higher responses than the square wave one. Both fast and secondary expressed proteins of c-fos and pp38 are measured from spinal cord tissue sectioning slides to characterize the pain associated inflammatory responses and their responses due to PRF stimulation.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2013
Seizure prediction with bipolar spectral power features using Adaboost and SVM classifiers.
This paper presents the results of our study on finding a lower complexity and yet a robust seizure prediction method using intracranial electroencephalogram (iEEG) recordings. We compare two classifiers: a low-complexity Adaboost and the more complex support vector machine (SVM). Adaboost is a linear classier using decision stumps, and SVM uses a nonlinear Gaussian kernel. ⋯ The proposed methods were applied on 8 invasive recordings selected from the EPILEPSIAE database, the European database of EEG seizure recordings. Doublecross validation is used by separating data sets for training and optimization from testing. The key conclusion is that Adaboost performs slightly better than SVM using a reduced feature set on average with significantly less complexity resulting in a sensitivity of 77.1% (27 of 35 seizures in 873 h recordings) and a false alarm rate of 0.18 per hour.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2013
A new method to increase the quality of cardiopulmonary resuscitation in hospital.
In order to ensure that high-quality cardio-pulmonary-resuscitation (CPR) is performed, many kinds of feedback devices have been developed that are helpful for achieving correct chest compression (CC) in manikin studies. However, the mattress compression depth (MCD) can cause overestimation of chest compression depth (CCD) during CPR using a feedback device. Herein, we propose a new method using a vinyl cover that encloses the foam mattress and is compressed by vacuum pump just before performing CPR, which could increase the performance of CCs during CPR.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2013
Combined use of sEMG and accelerometer in hand motion classification considering forearm rotation.
Hand motion classification using surface electromyography (sEMG) has been widely studied for its applications in upper-limb prosthesis and human-machine interface etc. Pattern-recognition based control methods have many advantages, and the reported classification accuracy can meet the requirements of practical applications. ⋯ In this paper, we give a pilot study of the reverse effect of forearm rotations on hand motion classification, and the results show that the forearm rotations can substantially degrade the classifier's performance: the average intra-position error is only 2.4%, but the average interposition classification error is as high as 44.0%. To solve this problem, we use an extra accelerometer to estimate the forearm rotation angles, and the best combination of sEMG data and accelerometer outputs can reduce the average classification error to 3.3%.